class_docstr: RecogTextDataset for text recognition.
  
  The annotation format can be both in jsonl and txt. If the annotation file
  is in jsonl format, it should be a list of dicts. If the annotation file
  is in txt format, it should be a list of lines.
  
  The annotation formats are shown as follows.
  - txt format
  .. code-block:: none
  
      ``test_img1.jpg OpenMMLab``
      ``test_img2.jpg MMOCR``
  
  - jsonl format
  .. code-block:: none
  
      ``{"filename": "test_img1.jpg", "text": "OpenMMLab"}``
      ``{"filename": "test_img2.jpg", "text": "MMOCR"}``
  
  Args:
      ann_file (str): Annotation file path. Defaults to ''.
      backend_args (dict, optional): Arguments to instantiate the
          prefix of uri corresponding backend. Defaults to None.
      parse_cfg (dict, optional): Config of parser for parsing annotations.
          Use ``LineJsonParser`` when the annotation file is in jsonl format
          with keys of ``filename`` and ``text``. The keys in parse_cfg
          should be consistent with the keys in jsonl annotations. The first
          key in parse_cfg should be the key of the path in jsonl
          annotations. The second key in parse_cfg should be the key of the
          text in jsonl Use ``LineStrParser`` when the annotation file is in
          txt format. Defaults to
          ``dict(type='LineJsonParser', keys=['filename', 'text'])``.
      metainfo (dict, optional): Meta information for dataset, such as class
          information. Defaults to None.
      data_root (str): The root directory for ``data_prefix`` and
          ``ann_file``. Defaults to ''.
      data_prefix (dict): Prefix for training data. Defaults to
          ``dict(img_path='')``.
      filter_cfg (dict, optional): Config for filter data. Defaults to None.
      indices (int or Sequence[int], optional): Support using first few
          data in annotation file to facilitate training/testing on a smaller
          dataset. Defaults to None which means using all ``data_infos``.
      serialize_data (bool, optional): Whether to hold memory using
          serialized objects, when enabled, data loader workers can use
          shared RAM from master process instead of making a copy. Defaults
          to True.
      pipeline (list, optional): Processing pipeline. Defaults to [].
      test_mode (bool, optional): ``test_mode=True`` means in test phase.
          Defaults to False.
      lazy_init (bool, optional): Whether to load annotation during
          instantiation. In some cases, such as visualization, only the meta
          information of the dataset is needed, which is not necessary to
          load annotation file. ``RecogTextDataset`` can skip load
          annotations to save time by set ``lazy_init=False``. Defaults to
          False.
      max_refetch (int, optional): If ``RecogTextDataset.prepare_data`` get a
          None img. The maximum extra number of cycles to get a valid
          image. Defaults to 1000.
docstr: Parse raw annotation to target format.
  
  Args:
      raw_anno_info (str): One raw data information loaded
          from ``ann_file``.
  
  Returns:
      (dict): Parsed annotation.
docstr_tok: ["Parse","raw","annotation","to","target","format",".","Args",":","raw_anno_info","(","str",")",":","One","raw","data","information","loaded","from","``","ann_file","``",".","Returns",":","(","dict",")",":","Parsed","annotation","."]
code: def parse_data_info(self, raw_anno_info: str) -> dict:
      """Parse raw annotation to target format.
  
      Args:
          raw_anno_info (str): One raw data information loaded
              from ``ann_file``.
  
      Returns:
          (dict): Parsed annotation.
      """
      data_info = {}
      parsed_anno = self.parser(raw_anno_info)
      img_path = osp.join(
          self.data_prefix["img_path"], parsed_anno[self.parser.keys[0]]
      )
  
      data_info["img_path"] = img_path
      data_info["instances"] = [
          dict(text=parsed_anno[self.parser.keys[1]])
      ]
      return data_info
  
file_path: mmocr/mmocr/datasets/recog_text_dataset.py
imports: import os.path
  from typing import Callable, List, Optional, Sequence, Union
  from mmengine.dataset import BaseDataset
  from mmengine.fileio import list_from_file
  from mmocr.registry import DATASETS, TASK_UTILS
label_desc: Use image node_id 3 for calling the RecogTextDataset obj's underlying member method code with example usage: obj.parse_data_info(raw_anno_info) and returns: data_info